PROJECT SUMMARY/ABSTRACT This project develops new computational analysis and modeling methods for DEER (double electron- electron resonance) spectroscopy. DEER is a biostructural technique for the quantification of protein conformational landscapes and protein motions on the nanometer scale. Protein motions are crucial for many key molecular processes at the basis of human life and disease. Therefore, DEER provides important insights that contribute to the knowledge base necessary for drug development. In combination with X-ray crystallography, NMR and cryo-EM, DEER is part of a complementary set of integrative experimental biostructural tools. It is especially important for the study of membrane proteins. A major barrier in the field is the lack of integrated analysis tools for biomedical researchers. This project addresses this issue by (a) developing methods and tools based on Bayesian statistics for the rigorous and reproducible analysis of experimental DEER data, providing comprehensive methods for uncertainty quantification (error bars) in DEER data, and (b) creating advanced computational approaches that utilize DEER data in the refinement of protein structures based on atomic-resolution ensemble models. Overall, the goal of the project is to significantly accelerate the workflow of DEER data analysis, interpretation, and modeling and to increase its rigor, reproducibility and accessibility. This will enable the study of the structure and dynamics of larger and more complex proteins and protein assemblies, which are increasingly important in biomedical research.